Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time

Yatong Han, Xiufen Ye, Jun Cheng, Siyuan Zhang, Weixing Feng, Zhi Han, Jie Zhang, Kun Huang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. Results: We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. Conclusions: We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis. Reviewers: Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev.

Original languageEnglish (US)
Article number4
JournalBiology Direct
Volume14
Issue number1
DOIs
StatePublished - Feb 13 2019

Fingerprint

Gene Regulatory Networks
Survival Time
Neuroblastoma
Gene expression
Gene Expression
gene expression
Microarrays
RNA
Module
prognosis
Survival
Prognosis
Workflow
Genes
Association reactions
Proportional Hazards Models
Lasso
Survival Rate
survival rate
Microarray

Keywords

  • Gene co-expression network
  • Integrative cluster
  • Neuroblastoma survival time predict

ASJC Scopus subject areas

  • Immunology
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time. / Han, Yatong; Ye, Xiufen; Cheng, Jun; Zhang, Siyuan; Feng, Weixing; Han, Zhi; Zhang, Jie; Huang, Kun.

In: Biology Direct, Vol. 14, No. 1, 4, 13.02.2019.

Research output: Contribution to journalArticle

Han, Yatong ; Ye, Xiufen ; Cheng, Jun ; Zhang, Siyuan ; Feng, Weixing ; Han, Zhi ; Zhang, Jie ; Huang, Kun. / Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time. In: Biology Direct. 2019 ; Vol. 14, No. 1.
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AU - Han, Yatong

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AU - Feng, Weixing

AU - Han, Zhi

AU - Zhang, Jie

AU - Huang, Kun

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AB - Background: More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. Results: We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. Conclusions: We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis. Reviewers: Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev.

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